Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add metagit-ai/metagit-cli --skill metagit-workspace-syncgit clone --depth 1 https://github.com/metagit-ai/metagit-cliWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/metagit-ai/metagit-cli/metagit-workspace-sync)<a href="https://agentmods.dev/skills/metagit-ai/metagit-cli/metagit-workspace-sync"><img src="https://agentmods.dev/badge/skills/metagit-ai/metagit-cli/metagit-workspace-sync/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/metagit-ai/metagit-cli/metagit-workspace-sync"><img src="https://agentmods.dev/badge/skills/metagit-ai/metagit-cli/metagit-workspace-sync.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00033 | $0.00258 |
| Opus 5 | $0.00016 | $0.00129 |
| Sonnet 5 | $0.00007 | $0.00052 |
| Haiku 4.5 | $0.00003 | $0.00026 |
Grade A, and why
metagit-workspace-sync scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Syncing Workspace Repositories
Use this skill when repository state must be updated.
Workflow
- Confirm active workspace and target project.
- Start with read-only status and fetch where possible.
- Use pull/clone only when required for the current objective.
- Summarize which repositories changed and what remains stale.
Commands
metagit project sync --project <name>- MCP
metagit_workspace_syncfor batch fetch/pull/clone (repos,only_if,dry_run,max_parallel) - MCP
metagit_repo_syncfor a single repository (requiresallow_mutationfor pull/clone) ./skills/metagit-control-center/scripts/control-cycle.sh [root_path] ["query"] [preset]
Output Contract
Return:
- repositories synced
- sync mode used (fetch/pull/clone)
- failures and retry guidance
Safety
- Limit sync to repos defined in
.metagit.yml. - Avoid broad synchronization if only a subset is needed.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 36 lines · 33 tokens per session scan A 1b0502244a70
metagit-workspace-sync is a skill published in the GitHub repository metagit-ai/metagit-cli (3 stars, last pushed today), licensed MIT. It adds 33 tokens to every session and 258 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
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perseus
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memory-commit
Use when the user explicitly says "remember this", "save this", "ghi nho", "luu lai", "save for next time", or otherwise asks to persist the immediately preceding context. Captures with the appropriate contexttype (decision, preference, fact, skill, task, conversation) so future sessions can retrieve it accurately.